Congxing Shi
Biographic Data
| ID | 7777334 |
|---|---|
| NAME | Congxing Shi |
| GIVEN NAMES | Congxing |
| FAMILY NAME | Shi |
| SIGNATURE | SHI C |
| AFFILIATIONS | Sun Yat-sen University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Development of Bayesian segmented Poisson regression model to forecast Covid-19 dynamics based on wastewater data
This study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks
The risk of injuries during work and its association with precipitation
Background Injuries during work are often exogenous and can be easily influenced by environmental factors, especially weather conditions. Precipitation, a crucial weather factor, has been linked to unintentional injuries, yet evidence of its effect on work-related injuries is limited. Therefore, we aimed to clarify the impact of precipitation on injuries during work as well as its variation across numerous vulnerability features. Methods Records …
What are the individual and joint impacts of key meteorological factors on the risk of unintentional injuries? A case-crossover study of over 147,800 cases from a sentinel-based surveillance system
No prominent works on this page.
The risk of injuries during work and its association with precipitation
Background Injuries during work are often exogenous and can be easily influenced by environmental factors, especially weather conditions. Precipitation, a crucial weather factor, has been linked to unintentional injuries, yet evidence of its effect on work-related injuries is limited. Therefore, we aimed to clarify the impact of precipitation on injuries during work as well as its variation across numerous vulnerability features. Methods Records …
What are the individual and joint impacts of key meteorological factors on the risk of unintentional injuries? A case-crossover study of over 147,800 cases from a sentinel-based surveillance system
Development of Bayesian segmented Poisson regression model to forecast Covid-19 dynamics based on wastewater data
This study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks
Environmental health (3 works) · Geography (3 works) · Medicine (3 works) · Climate Change and Health Impacts (2 works) · Demography (2 works) · Meteorology (2 works) · Precipitation (2 works) · 2019-20 coronavirus outbreak (1 work) · Bayesian probability (1 work) · Biostatistics (1 work)